جستجوی استاد راهنمای دانشگاهی

Walter Bierbauerمشاهده پروفایل

پژوهشگر

• Health Psychology
• Behavior Change
• Chronic Disease Management
• +۶ مورد دیگر

Walter Bierbauer, Dr. phil., is a postdoctoral researcher in the Department of Psychology – Applied Social and Health Psychology at the University of Zurich, Faculty of Arts and Social Sciences. He is also an active member of the interdisciplinary Digital Society Initiative (DSI) Community Health cluster at the same university. His research centres on motivational and volitional determinants of health behaviour change among individuals living with chronic conditions. Employing intensive longitudinal designs, digital monitoring tools, and advanced statistical modelling, he investigates how psychological factors such as self-efficacy, social support, and illness perceptions translate into day-to-day variations in physical activity, medication adherence, and coping behaviours. Recent work focuses on cardiac rehabilitation, long-COVID, and breast-cancer survivors undergoing adjuvant endocrine therapy. Across 29 peer-reviewed publications (2015-2024), Bierbauer has advanced the Health Action Process Approach, uncovered differential impacts of depression and anxiety on rehabilitation outcomes, and highlighted the moderating roles of self-reliance preferences and social stigma. These contributions bridge clinical psychology, behavioural medicine, and digital-health methodologies. Labs & Collaborations: Applied Social and Health Psychology group, University of Zurich Digital Society Initiative (DSI) Community Health Contact: walter.bierbauer@psychologie.uzh.ch

• Computer Vision
• Deep Learning
• Image Processing
• +۳ مورد دیگر

Chabbi Houda is a Full Professor at the Fribourg School of Engineering and Architecture, University of Applied Sciences and Arts Western Switzerland. She leads research at the iCoSys Institute for Complex Systems, focusing on AI-driven solutions for industrial and societal challenges. Her work bridges computer vision, deep learning, and practical applications from medical imaging to construction materials. Her research explores: Advanced computer vision for industrial inspection and monitoring Deep learning architectures for anomaly detection and predictive modeling Multimodal data fusion in surveillance systems AI applications in material science and healthcare She leads significant projects including: HIGH-ROAD (2025-2026): Hierarchical AI for road asset inventory using orthoimagery VideoCognition (2023-2025): Explainable AI for surveillance systems with Morphean SA SoNIT (2025-2028): Sonography nerve tracking with FHNW Concrete AI (2022-2025): Machine learning for recycled concrete optimization Her lab at iCoSys Institute collaborates with academic partners including University of Applied Sciences Northwestern Switzerland and industry leaders in medical tech, surveillance, and construction.

Anastasios Kouvelasمشاهده پروفایل

مدرس

• Traffic flow theory
• System and control theory
• Optimization
• +۱۰ مورد دیگر

Dr. Anastasios Kouvelas is a Lecturer at ETH Zurich, where he serves as head of the Road Traffic Engineering research group at the Institute of Transport Planning and Systems (IVT), Department of Civil, Environmental and Geomatic Engineering. He has held this position since August 2018, succeeding Dr. Monica Menendez who moved to New York University in Abu Dhabi. Prior to joining ETH Zurich, he was a research associate at the Urban Transport Systems Laboratory (LUTS) at EPFL (2014-2018) and a postdoctoral fellow at Partners for Advanced Transportation Technology (PATH) at the University of California, Berkeley (2012-2014). Dr. Kouvelas' research focuses on modeling, simulation, optimization and traffic flow control. His work aims to develop real-time solutions based on control theory and operations research methods. The Road Traffic Engineering group develops algorithmic solutions that are components of intelligent transportation systems used in traffic control centers. Recent technological advances in autonomous vehicles have expanded their research topics as the industry seeks efficient operational solutions for autonomous mobility. They are particularly interested in extending their work to the design of advanced management strategies for urban networks that utilize connected vehicles to improve traffic operations and develop network-wide control strategies that minimize environmental impacts. His recent publications (2023-2025) demonstrate strong focus on traffic prediction using deep learning techniques, bike lane allocation impacts on urban networks, transit network resilience against disruptions, vehicle trajectory extraction from aerial recordings, and traffic control for mixed traffic systems with connected and autonomous vehicles. His work bridges theoretical developments in control theory with practical traffic engineering challenges. Scientific Awards No specific scientific awards were mentioned in the provided information. Advising and Grants Dr. Kouvelas supervises PhD and Master's students in traffic engineering and intelligent transportation systems. His research is supported by various grants including a grant from the Hong Kong Research Grant Council (Grant No. GRF 11216323) for research on traffic speed prediction. Laboratories and Teams Dr. Kouvelas leads the multidisciplinary Road Traffic Engineering research group at IVT, which consists of researchers with backgrounds in civil engineering, electrical engineering, mechanical engineering, computer science, control, and operations research. The group's work spans multiple areas including traffic flow theory, traffic operations, connected and automated vehicles, and intelligent transportation systems.